{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm_notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"train.csv\")\n",
    "test_df = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200000, 202), (200000, 201))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.sample(frac=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df=test_df.sample(frac=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4000, 202), (4000, 201))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172212</th>\n",
       "      <td>train_172212</td>\n",
       "      <td>0</td>\n",
       "      <td>10.3687</td>\n",
       "      <td>3.7804</td>\n",
       "      <td>8.5036</td>\n",
       "      <td>9.1164</td>\n",
       "      <td>10.9905</td>\n",
       "      <td>-11.2245</td>\n",
       "      <td>4.8282</td>\n",
       "      <td>15.8429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2880</td>\n",
       "      <td>8.6330</td>\n",
       "      <td>-0.0707</td>\n",
       "      <td>9.5651</td>\n",
       "      <td>22.6890</td>\n",
       "      <td>-1.3634</td>\n",
       "      <td>8.5586</td>\n",
       "      <td>6.8578</td>\n",
       "      <td>15.9635</td>\n",
       "      <td>-8.0137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195563</th>\n",
       "      <td>train_195563</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0062</td>\n",
       "      <td>-6.7854</td>\n",
       "      <td>7.0029</td>\n",
       "      <td>5.2939</td>\n",
       "      <td>8.3098</td>\n",
       "      <td>-12.8027</td>\n",
       "      <td>5.4212</td>\n",
       "      <td>17.8903</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4023</td>\n",
       "      <td>7.7749</td>\n",
       "      <td>1.4374</td>\n",
       "      <td>2.0666</td>\n",
       "      <td>17.6930</td>\n",
       "      <td>2.3123</td>\n",
       "      <td>5.3959</td>\n",
       "      <td>10.7631</td>\n",
       "      <td>21.4181</td>\n",
       "      <td>7.0175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141697</th>\n",
       "      <td>train_141697</td>\n",
       "      <td>0</td>\n",
       "      <td>4.9974</td>\n",
       "      <td>0.6442</td>\n",
       "      <td>12.0433</td>\n",
       "      <td>8.9549</td>\n",
       "      <td>10.1382</td>\n",
       "      <td>-10.8972</td>\n",
       "      <td>6.6268</td>\n",
       "      <td>20.5790</td>\n",
       "      <td>...</td>\n",
       "      <td>3.4336</td>\n",
       "      <td>10.0835</td>\n",
       "      <td>2.4379</td>\n",
       "      <td>4.3614</td>\n",
       "      <td>12.0469</td>\n",
       "      <td>-1.5565</td>\n",
       "      <td>8.4857</td>\n",
       "      <td>7.6009</td>\n",
       "      <td>15.2204</td>\n",
       "      <td>4.3415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11577</th>\n",
       "      <td>train_11577</td>\n",
       "      <td>0</td>\n",
       "      <td>8.1876</td>\n",
       "      <td>7.0007</td>\n",
       "      <td>14.5394</td>\n",
       "      <td>8.1311</td>\n",
       "      <td>9.5619</td>\n",
       "      <td>-8.8410</td>\n",
       "      <td>6.7868</td>\n",
       "      <td>16.9666</td>\n",
       "      <td>...</td>\n",
       "      <td>3.9039</td>\n",
       "      <td>7.0508</td>\n",
       "      <td>3.5504</td>\n",
       "      <td>7.0111</td>\n",
       "      <td>15.0668</td>\n",
       "      <td>-1.4495</td>\n",
       "      <td>5.1872</td>\n",
       "      <td>8.5501</td>\n",
       "      <td>17.5519</td>\n",
       "      <td>2.1677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118173</th>\n",
       "      <td>train_118173</td>\n",
       "      <td>0</td>\n",
       "      <td>10.3889</td>\n",
       "      <td>-4.3281</td>\n",
       "      <td>10.0079</td>\n",
       "      <td>3.9148</td>\n",
       "      <td>10.8781</td>\n",
       "      <td>3.5158</td>\n",
       "      <td>4.8513</td>\n",
       "      <td>11.2542</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3964</td>\n",
       "      <td>5.3486</td>\n",
       "      <td>5.0895</td>\n",
       "      <td>-2.3984</td>\n",
       "      <td>15.4571</td>\n",
       "      <td>0.1488</td>\n",
       "      <td>10.0558</td>\n",
       "      <td>9.5458</td>\n",
       "      <td>14.8031</td>\n",
       "      <td>12.3660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID_code  target    var_0   var_1    var_2   var_3    var_4  \\\n",
       "172212  train_172212       0  10.3687  3.7804   8.5036  9.1164  10.9905   \n",
       "195563  train_195563       0  12.0062 -6.7854   7.0029  5.2939   8.3098   \n",
       "141697  train_141697       0   4.9974  0.6442  12.0433  8.9549  10.1382   \n",
       "11577    train_11577       0   8.1876  7.0007  14.5394  8.1311   9.5619   \n",
       "118173  train_118173       0  10.3889 -4.3281  10.0079  3.9148  10.8781   \n",
       "\n",
       "          var_5   var_6    var_7   ...     var_190  var_191  var_192  var_193  \\\n",
       "172212 -11.2245  4.8282  15.8429   ...      0.2880   8.6330  -0.0707   9.5651   \n",
       "195563 -12.8027  5.4212  17.8903   ...      9.4023   7.7749   1.4374   2.0666   \n",
       "141697 -10.8972  6.6268  20.5790   ...      3.4336  10.0835   2.4379   4.3614   \n",
       "11577   -8.8410  6.7868  16.9666   ...      3.9039   7.0508   3.5504   7.0111   \n",
       "118173   3.5158  4.8513  11.2542   ...      3.3964   5.3486   5.0895  -2.3984   \n",
       "\n",
       "        var_194  var_195  var_196  var_197  var_198  var_199  \n",
       "172212  22.6890  -1.3634   8.5586   6.8578  15.9635  -8.0137  \n",
       "195563  17.6930   2.3123   5.3959  10.7631  21.4181   7.0175  \n",
       "141697  12.0469  -1.5565   8.4857   7.6009  15.2204   4.3415  \n",
       "11577   15.0668  -1.4495   5.1872   8.5501  17.5519   2.1677  \n",
       "118173  15.4571   0.1488  10.0558   9.5458  14.8031  12.3660  \n",
       "\n",
       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID_code</th>\n",
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       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
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       "      <th>var_196</th>\n",
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       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111742</th>\n",
       "      <td>test_111742</td>\n",
       "      <td>7.1017</td>\n",
       "      <td>-7.0226</td>\n",
       "      <td>8.8662</td>\n",
       "      <td>4.4112</td>\n",
       "      <td>12.9127</td>\n",
       "      <td>-8.3927</td>\n",
       "      <td>5.0562</td>\n",
       "      <td>20.7294</td>\n",
       "      <td>-5.7492</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.0424</td>\n",
       "      <td>7.3302</td>\n",
       "      <td>2.2614</td>\n",
       "      <td>1.4137</td>\n",
       "      <td>14.5809</td>\n",
       "      <td>0.4343</td>\n",
       "      <td>-0.9775</td>\n",
       "      <td>8.0451</td>\n",
       "      <td>17.5427</td>\n",
       "      <td>1.9752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134584</th>\n",
       "      <td>test_134584</td>\n",
       "      <td>7.5326</td>\n",
       "      <td>-3.5924</td>\n",
       "      <td>9.5202</td>\n",
       "      <td>6.9907</td>\n",
       "      <td>11.8528</td>\n",
       "      <td>-9.9475</td>\n",
       "      <td>4.4712</td>\n",
       "      <td>18.4932</td>\n",
       "      <td>-0.1544</td>\n",
       "      <td>...</td>\n",
       "      <td>8.1326</td>\n",
       "      <td>9.3324</td>\n",
       "      <td>3.0095</td>\n",
       "      <td>-6.6260</td>\n",
       "      <td>18.7246</td>\n",
       "      <td>1.3412</td>\n",
       "      <td>-6.1256</td>\n",
       "      <td>9.4191</td>\n",
       "      <td>17.5112</td>\n",
       "      <td>-7.8832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171576</th>\n",
       "      <td>test_171576</td>\n",
       "      <td>11.9295</td>\n",
       "      <td>5.0319</td>\n",
       "      <td>12.2931</td>\n",
       "      <td>6.4719</td>\n",
       "      <td>9.3988</td>\n",
       "      <td>2.1542</td>\n",
       "      <td>7.0431</td>\n",
       "      <td>20.8070</td>\n",
       "      <td>4.2349</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.3971</td>\n",
       "      <td>7.5152</td>\n",
       "      <td>3.6517</td>\n",
       "      <td>4.1056</td>\n",
       "      <td>19.5447</td>\n",
       "      <td>-1.0038</td>\n",
       "      <td>8.6398</td>\n",
       "      <td>8.5203</td>\n",
       "      <td>13.9277</td>\n",
       "      <td>8.4888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>test_18248</td>\n",
       "      <td>12.5517</td>\n",
       "      <td>-5.6769</td>\n",
       "      <td>10.6612</td>\n",
       "      <td>9.0976</td>\n",
       "      <td>7.3118</td>\n",
       "      <td>-7.1585</td>\n",
       "      <td>5.2158</td>\n",
       "      <td>16.5374</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>...</td>\n",
       "      <td>7.8691</td>\n",
       "      <td>9.3100</td>\n",
       "      <td>2.0380</td>\n",
       "      <td>8.5298</td>\n",
       "      <td>13.2488</td>\n",
       "      <td>1.4744</td>\n",
       "      <td>-4.2166</td>\n",
       "      <td>9.1001</td>\n",
       "      <td>16.6001</td>\n",
       "      <td>-12.2388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168598</th>\n",
       "      <td>test_168598</td>\n",
       "      <td>5.2550</td>\n",
       "      <td>-5.7010</td>\n",
       "      <td>10.4170</td>\n",
       "      <td>4.3574</td>\n",
       "      <td>11.1402</td>\n",
       "      <td>4.2977</td>\n",
       "      <td>4.3163</td>\n",
       "      <td>15.1148</td>\n",
       "      <td>1.6831</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0984</td>\n",
       "      <td>3.4144</td>\n",
       "      <td>2.7513</td>\n",
       "      <td>4.4794</td>\n",
       "      <td>18.6202</td>\n",
       "      <td>0.8962</td>\n",
       "      <td>-0.4968</td>\n",
       "      <td>9.5542</td>\n",
       "      <td>15.3902</td>\n",
       "      <td>-19.9531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID_code    var_0   var_1    var_2   var_3    var_4   var_5  \\\n",
       "111742  test_111742   7.1017 -7.0226   8.8662  4.4112  12.9127 -8.3927   \n",
       "134584  test_134584   7.5326 -3.5924   9.5202  6.9907  11.8528 -9.9475   \n",
       "171576  test_171576  11.9295  5.0319  12.2931  6.4719   9.3988  2.1542   \n",
       "18248    test_18248  12.5517 -5.6769  10.6612  9.0976   7.3118 -7.1585   \n",
       "168598  test_168598   5.2550 -5.7010  10.4170  4.3574  11.1402  4.2977   \n",
       "\n",
       "         var_6    var_7   var_8   ...     var_190  var_191  var_192  var_193  \\\n",
       "111742  5.0562  20.7294 -5.7492   ...     -2.0424   7.3302   2.2614   1.4137   \n",
       "134584  4.4712  18.4932 -0.1544   ...      8.1326   9.3324   3.0095  -6.6260   \n",
       "171576  7.0431  20.8070  4.2349   ...     -3.3971   7.5152   3.6517   4.1056   \n",
       "18248   5.2158  16.5374  0.0121   ...      7.8691   9.3100   2.0380   8.5298   \n",
       "168598  4.3163  15.1148  1.6831   ...      1.0984   3.4144   2.7513   4.4794   \n",
       "\n",
       "        var_194  var_195  var_196  var_197  var_198  var_199  \n",
       "111742  14.5809   0.4343  -0.9775   8.0451  17.5427   1.9752  \n",
       "134584  18.7246   1.3412  -6.1256   9.4191  17.5112  -7.8832  \n",
       "171576  19.5447  -1.0038   8.6398   8.5203  13.9277   8.4888  \n",
       "18248   13.2488   1.4744  -4.2166   9.1001  16.6001 -12.2388  \n",
       "168598  18.6202   0.8962  -0.4968   9.5542  15.3902 -19.9531  \n",
       "\n",
       "[5 rows x 201 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_data(data):\n",
    "    total = data.isnull().sum()\n",
    "    percent = (data.isnull().sum()/data.isnull().count()*100)\n",
    "    tt = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    types = []\n",
    "    for col in data.columns:\n",
    "        dtype = str(data[col].dtype)\n",
    "        types.append(dtype)\n",
    "    tt['Types'] = types\n",
    "    return(np.transpose(tt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID_code</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "      <th>var_8</th>\n",
       "      <th>...</th>\n",
       "      <th>var_190</th>\n",
       "      <th>var_191</th>\n",
       "      <th>var_192</th>\n",
       "      <th>var_193</th>\n",
       "      <th>var_194</th>\n",
       "      <th>var_195</th>\n",
       "      <th>var_196</th>\n",
       "      <th>var_197</th>\n",
       "      <th>var_198</th>\n",
       "      <th>var_199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Percent</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Types</th>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>...</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 201 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID_code    var_0    var_1    var_2    var_3    var_4    var_5  \\\n",
       "Total         0        0        0        0        0        0        0   \n",
       "Percent       0        0        0        0        0        0        0   \n",
       "Types    object  float64  float64  float64  float64  float64  float64   \n",
       "\n",
       "           var_6    var_7    var_8   ...     var_190  var_191  var_192  \\\n",
       "Total          0        0        0   ...           0        0        0   \n",
       "Percent        0        0        0   ...           0        0        0   \n",
       "Types    float64  float64  float64   ...     float64  float64  float64   \n",
       "\n",
       "         var_193  var_194  var_195  var_196  var_197  var_198  var_199  \n",
       "Total          0        0        0        0        0        0        0  \n",
       "Percent        0        0        0        0        0        0        0  \n",
       "Types    float64  float64  float64  float64  float64  float64  float64  \n",
       "\n",
       "[3 rows x 201 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
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       "      <th>var_8</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
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       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
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       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.101000</td>\n",
       "      <td>10.679887</td>\n",
       "      <td>-1.643497</td>\n",
       "      <td>10.693089</td>\n",
       "      <td>6.790406</td>\n",
       "      <td>11.124402</td>\n",
       "      <td>-4.738295</td>\n",
       "      <td>5.389072</td>\n",
       "      <td>16.566263</td>\n",
       "      <td>0.253911</td>\n",
       "      <td>...</td>\n",
       "      <td>3.185891</td>\n",
       "      <td>7.315337</td>\n",
       "      <td>1.925283</td>\n",
       "      <td>3.462431</td>\n",
       "      <td>17.897179</td>\n",
       "      <td>-0.122319</td>\n",
       "      <td>2.330021</td>\n",
       "      <td>8.918595</td>\n",
       "      <td>15.883699</td>\n",
       "      <td>-3.309744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.301366</td>\n",
       "      <td>3.052061</td>\n",
       "      <td>4.025690</td>\n",
       "      <td>2.615990</td>\n",
       "      <td>2.021904</td>\n",
       "      <td>1.634615</td>\n",
       "      <td>7.792491</td>\n",
       "      <td>0.865157</td>\n",
       "      <td>3.434691</td>\n",
       "      <td>3.343286</td>\n",
       "      <td>...</td>\n",
       "      <td>4.450573</td>\n",
       "      <td>2.926189</td>\n",
       "      <td>1.467666</td>\n",
       "      <td>3.984534</td>\n",
       "      <td>3.098593</td>\n",
       "      <td>1.425531</td>\n",
       "      <td>5.447600</td>\n",
       "      <td>0.914260</td>\n",
       "      <td>3.014243</td>\n",
       "      <td>10.417619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.582200</td>\n",
       "      <td>-13.361300</td>\n",
       "      <td>2.582500</td>\n",
       "      <td>0.930100</td>\n",
       "      <td>6.197200</td>\n",
       "      <td>-25.210500</td>\n",
       "      <td>2.912300</td>\n",
       "      <td>7.119500</td>\n",
       "      <td>-8.401900</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.802600</td>\n",
       "      <td>-0.757300</td>\n",
       "      <td>-2.985400</td>\n",
       "      <td>-8.922900</td>\n",
       "      <td>10.343500</td>\n",
       "      <td>-4.911600</td>\n",
       "      <td>-12.711200</td>\n",
       "      <td>6.362600</td>\n",
       "      <td>7.394400</td>\n",
       "      <td>-34.895200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.408650</td>\n",
       "      <td>-4.598000</td>\n",
       "      <td>8.687550</td>\n",
       "      <td>5.284950</td>\n",
       "      <td>9.895325</td>\n",
       "      <td>-10.853200</td>\n",
       "      <td>4.731750</td>\n",
       "      <td>13.952575</td>\n",
       "      <td>-2.409225</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048200</td>\n",
       "      <td>5.095900</td>\n",
       "      <td>0.896775</td>\n",
       "      <td>0.648650</td>\n",
       "      <td>15.582175</td>\n",
       "      <td>-1.154500</td>\n",
       "      <td>-1.742525</td>\n",
       "      <td>8.271475</td>\n",
       "      <td>13.819450</td>\n",
       "      <td>-11.184375</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.501350</td>\n",
       "      <td>-1.623650</td>\n",
       "      <td>10.555500</td>\n",
       "      <td>6.822500</td>\n",
       "      <td>11.196850</td>\n",
       "      <td>-4.459800</td>\n",
       "      <td>5.381650</td>\n",
       "      <td>16.490850</td>\n",
       "      <td>0.384650</td>\n",
       "      <td>...</td>\n",
       "      <td>3.174700</td>\n",
       "      <td>7.253000</td>\n",
       "      <td>1.889950</td>\n",
       "      <td>3.470200</td>\n",
       "      <td>17.777600</td>\n",
       "      <td>-0.147950</td>\n",
       "      <td>2.330400</td>\n",
       "      <td>8.904150</td>\n",
       "      <td>15.954350</td>\n",
       "      <td>-3.019350</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.775325</td>\n",
       "      <td>1.292575</td>\n",
       "      <td>12.543400</td>\n",
       "      <td>8.302150</td>\n",
       "      <td>12.325250</td>\n",
       "      <td>1.281350</td>\n",
       "      <td>5.988750</td>\n",
       "      <td>19.167275</td>\n",
       "      <td>2.955900</td>\n",
       "      <td>...</td>\n",
       "      <td>6.299325</td>\n",
       "      <td>9.345850</td>\n",
       "      <td>2.945725</td>\n",
       "      <td>6.342925</td>\n",
       "      <td>20.183625</td>\n",
       "      <td>0.832475</td>\n",
       "      <td>6.574300</td>\n",
       "      <td>9.611225</td>\n",
       "      <td>18.086050</td>\n",
       "      <td>4.640150</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.379300</td>\n",
       "      <td>9.029800</td>\n",
       "      <td>18.000200</td>\n",
       "      <td>12.329700</td>\n",
       "      <td>15.991400</td>\n",
       "      <td>15.955100</td>\n",
       "      <td>7.935900</td>\n",
       "      <td>26.690400</td>\n",
       "      <td>9.482200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.889400</td>\n",
       "      <td>16.409400</td>\n",
       "      <td>6.818100</td>\n",
       "      <td>15.168200</td>\n",
       "      <td>26.628500</td>\n",
       "      <td>3.764700</td>\n",
       "      <td>14.572400</td>\n",
       "      <td>11.728100</td>\n",
       "      <td>24.808000</td>\n",
       "      <td>24.249500</td>\n",
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       "<p>8 rows × 201 columns</p>\n",
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      ],
      "text/plain": [
       "            target        var_0        var_1        var_2        var_3  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean      0.101000    10.679887    -1.643497    10.693089     6.790406   \n",
       "std       0.301366     3.052061     4.025690     2.615990     2.021904   \n",
       "min       0.000000     1.582200   -13.361300     2.582500     0.930100   \n",
       "25%       0.000000     8.408650    -4.598000     8.687550     5.284950   \n",
       "50%       0.000000    10.501350    -1.623650    10.555500     6.822500   \n",
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       "max       1.000000    19.379300     9.029800    18.000200    12.329700   \n",
       "\n",
       "             var_4        var_5        var_6        var_7        var_8  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean     11.124402    -4.738295     5.389072    16.566263     0.253911   \n",
       "std       1.634615     7.792491     0.865157     3.434691     3.343286   \n",
       "min       6.197200   -25.210500     2.912300     7.119500    -8.401900   \n",
       "25%       9.895325   -10.853200     4.731750    13.952575    -2.409225   \n",
       "50%      11.196850    -4.459800     5.381650    16.490850     0.384650   \n",
       "75%      12.325250     1.281350     5.988750    19.167275     2.955900   \n",
       "max      15.991400    15.955100     7.935900    26.690400     9.482200   \n",
       "\n",
       "          ...           var_190      var_191      var_192      var_193  \\\n",
       "count     ...       4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean      ...          3.185891     7.315337     1.925283     3.462431   \n",
       "std       ...          4.450573     2.926189     1.467666     3.984534   \n",
       "min       ...        -11.802600    -0.757300    -2.985400    -8.922900   \n",
       "25%       ...         -0.048200     5.095900     0.896775     0.648650   \n",
       "50%       ...          3.174700     7.253000     1.889950     3.470200   \n",
       "75%       ...          6.299325     9.345850     2.945725     6.342925   \n",
       "max       ...         14.889400    16.409400     6.818100    15.168200   \n",
       "\n",
       "           var_194      var_195      var_196      var_197      var_198  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean     17.897179    -0.122319     2.330021     8.918595    15.883699   \n",
       "std       3.098593     1.425531     5.447600     0.914260     3.014243   \n",
       "min      10.343500    -4.911600   -12.711200     6.362600     7.394400   \n",
       "25%      15.582175    -1.154500    -1.742525     8.271475    13.819450   \n",
       "50%      17.777600    -0.147950     2.330400     8.904150    15.954350   \n",
       "75%      20.183625     0.832475     6.574300     9.611225    18.086050   \n",
       "max      26.628500     3.764700    14.572400    11.728100    24.808000   \n",
       "\n",
       "           var_199  \n",
       "count  4000.000000  \n",
       "mean     -3.309744  \n",
       "std      10.417619  \n",
       "min     -34.895200  \n",
       "25%     -11.184375  \n",
       "50%      -3.019350  \n",
       "75%       4.640150  \n",
       "max      24.249500  \n",
       "\n",
       "[8 rows x 201 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.705145</td>\n",
       "      <td>-1.611127</td>\n",
       "      <td>10.744332</td>\n",
       "      <td>6.750937</td>\n",
       "      <td>11.056738</td>\n",
       "      <td>-4.991236</td>\n",
       "      <td>5.411349</td>\n",
       "      <td>16.523703</td>\n",
       "      <td>0.266786</td>\n",
       "      <td>7.571801</td>\n",
       "      <td>...</td>\n",
       "      <td>3.320239</td>\n",
       "      <td>7.496931</td>\n",
       "      <td>1.905329</td>\n",
       "      <td>3.358701</td>\n",
       "      <td>18.069424</td>\n",
       "      <td>-0.112999</td>\n",
       "      <td>2.291493</td>\n",
       "      <td>8.908916</td>\n",
       "      <td>15.821381</td>\n",
       "      <td>-3.561816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.044459</td>\n",
       "      <td>4.065361</td>\n",
       "      <td>2.671047</td>\n",
       "      <td>2.058261</td>\n",
       "      <td>1.626571</td>\n",
       "      <td>7.641259</td>\n",
       "      <td>0.880935</td>\n",
       "      <td>3.449726</td>\n",
       "      <td>3.306510</td>\n",
       "      <td>1.226856</td>\n",
       "      <td>...</td>\n",
       "      <td>4.576824</td>\n",
       "      <td>3.048856</td>\n",
       "      <td>1.468831</td>\n",
       "      <td>3.976750</td>\n",
       "      <td>3.133932</td>\n",
       "      <td>1.429607</td>\n",
       "      <td>5.407538</td>\n",
       "      <td>0.921277</td>\n",
       "      <td>3.024829</td>\n",
       "      <td>10.362907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.064200</td>\n",
       "      <td>-12.156100</td>\n",
       "      <td>3.299300</td>\n",
       "      <td>0.877800</td>\n",
       "      <td>6.382900</td>\n",
       "      <td>-25.770900</td>\n",
       "      <td>2.314800</td>\n",
       "      <td>8.152500</td>\n",
       "      <td>-8.701000</td>\n",
       "      <td>4.367200</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.639500</td>\n",
       "      <td>-0.522400</td>\n",
       "      <td>-3.244600</td>\n",
       "      <td>-9.939200</td>\n",
       "      <td>10.382900</td>\n",
       "      <td>-4.617500</td>\n",
       "      <td>-11.180300</td>\n",
       "      <td>6.401900</td>\n",
       "      <td>7.142500</td>\n",
       "      <td>-33.058300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.453725</td>\n",
       "      <td>-4.722150</td>\n",
       "      <td>8.748900</td>\n",
       "      <td>5.188075</td>\n",
       "      <td>9.863450</td>\n",
       "      <td>-10.836050</td>\n",
       "      <td>4.762425</td>\n",
       "      <td>13.884425</td>\n",
       "      <td>-2.363250</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012400</td>\n",
       "      <td>5.183350</td>\n",
       "      <td>0.889800</td>\n",
       "      <td>0.725100</td>\n",
       "      <td>15.711900</td>\n",
       "      <td>-1.136425</td>\n",
       "      <td>-1.967375</td>\n",
       "      <td>8.250775</td>\n",
       "      <td>13.825125</td>\n",
       "      <td>-11.492775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.616950</td>\n",
       "      <td>-1.635850</td>\n",
       "      <td>10.660800</td>\n",
       "      <td>6.783200</td>\n",
       "      <td>11.075150</td>\n",
       "      <td>-4.805050</td>\n",
       "      <td>5.370400</td>\n",
       "      <td>16.475350</td>\n",
       "      <td>0.322150</td>\n",
       "      <td>7.653800</td>\n",
       "      <td>...</td>\n",
       "      <td>3.373800</td>\n",
       "      <td>7.375150</td>\n",
       "      <td>1.873450</td>\n",
       "      <td>3.502600</td>\n",
       "      <td>18.099800</td>\n",
       "      <td>-0.153000</td>\n",
       "      <td>2.548450</td>\n",
       "      <td>8.887350</td>\n",
       "      <td>15.890200</td>\n",
       "      <td>-3.087850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.744025</td>\n",
       "      <td>1.425800</td>\n",
       "      <td>12.619650</td>\n",
       "      <td>8.341800</td>\n",
       "      <td>12.231275</td>\n",
       "      <td>0.644625</td>\n",
       "      <td>6.025450</td>\n",
       "      <td>19.000550</td>\n",
       "      <td>2.926450</td>\n",
       "      <td>8.563175</td>\n",
       "      <td>...</td>\n",
       "      <td>6.439900</td>\n",
       "      <td>9.455350</td>\n",
       "      <td>2.952625</td>\n",
       "      <td>6.158350</td>\n",
       "      <td>20.474600</td>\n",
       "      <td>0.840250</td>\n",
       "      <td>6.444875</td>\n",
       "      <td>9.583375</td>\n",
       "      <td>18.024375</td>\n",
       "      <td>4.636025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.287500</td>\n",
       "      <td>8.401000</td>\n",
       "      <td>18.206400</td>\n",
       "      <td>13.142000</td>\n",
       "      <td>15.340100</td>\n",
       "      <td>15.676800</td>\n",
       "      <td>7.858800</td>\n",
       "      <td>26.713900</td>\n",
       "      <td>8.927400</td>\n",
       "      <td>10.913000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.479400</td>\n",
       "      <td>16.256700</td>\n",
       "      <td>6.748700</td>\n",
       "      <td>16.241500</td>\n",
       "      <td>26.601800</td>\n",
       "      <td>3.486100</td>\n",
       "      <td>15.862700</td>\n",
       "      <td>11.542100</td>\n",
       "      <td>24.882800</td>\n",
       "      <td>22.402200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             var_0        var_1        var_2        var_3        var_4  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean     10.705145    -1.611127    10.744332     6.750937    11.056738   \n",
       "std       3.044459     4.065361     2.671047     2.058261     1.626571   \n",
       "min       3.064200   -12.156100     3.299300     0.877800     6.382900   \n",
       "25%       8.453725    -4.722150     8.748900     5.188075     9.863450   \n",
       "50%      10.616950    -1.635850    10.660800     6.783200    11.075150   \n",
       "75%      12.744025     1.425800    12.619650     8.341800    12.231275   \n",
       "max      19.287500     8.401000    18.206400    13.142000    15.340100   \n",
       "\n",
       "             var_5        var_6        var_7        var_8        var_9  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean     -4.991236     5.411349    16.523703     0.266786     7.571801   \n",
       "std       7.641259     0.880935     3.449726     3.306510     1.226856   \n",
       "min     -25.770900     2.314800     8.152500    -8.701000     4.367200   \n",
       "25%     -10.836050     4.762425    13.884425    -2.363250     6.623500   \n",
       "50%      -4.805050     5.370400    16.475350     0.322150     7.653800   \n",
       "75%       0.644625     6.025450    19.000550     2.926450     8.563175   \n",
       "max      15.676800     7.858800    26.713900     8.927400    10.913000   \n",
       "\n",
       "          ...           var_190      var_191      var_192      var_193  \\\n",
       "count     ...       4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean      ...          3.320239     7.496931     1.905329     3.358701   \n",
       "std       ...          4.576824     3.048856     1.468831     3.976750   \n",
       "min       ...        -10.639500    -0.522400    -3.244600    -9.939200   \n",
       "25%       ...         -0.012400     5.183350     0.889800     0.725100   \n",
       "50%       ...          3.373800     7.375150     1.873450     3.502600   \n",
       "75%       ...          6.439900     9.455350     2.952625     6.158350   \n",
       "max       ...         18.479400    16.256700     6.748700    16.241500   \n",
       "\n",
       "           var_194      var_195      var_196      var_197      var_198  \\\n",
       "count  4000.000000  4000.000000  4000.000000  4000.000000  4000.000000   \n",
       "mean     18.069424    -0.112999     2.291493     8.908916    15.821381   \n",
       "std       3.133932     1.429607     5.407538     0.921277     3.024829   \n",
       "min      10.382900    -4.617500   -11.180300     6.401900     7.142500   \n",
       "25%      15.711900    -1.136425    -1.967375     8.250775    13.825125   \n",
       "50%      18.099800    -0.153000     2.548450     8.887350    15.890200   \n",
       "75%      20.474600     0.840250     6.444875     9.583375    18.024375   \n",
       "max      26.601800     3.486100    15.862700    11.542100    24.882800   \n",
       "\n",
       "           var_199  \n",
       "count  4000.000000  \n",
       "mean     -3.561816  \n",
       "std      10.362907  \n",
       "min     -33.058300  \n",
       "25%     -11.492775  \n",
       "50%      -3.087850  \n",
       "75%       4.636025  \n",
       "max      22.402200  \n",
       "\n",
       "[8 rows x 200 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_scatter(df1, df2, features):\n",
    "    i = 0\n",
    "    sns.set_style('whitegrid')\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots(4,4,figsize=(14,14))\n",
    "\n",
    "    for feature in features:\n",
    "        i += 1\n",
    "        plt.subplot(4,4,i)\n",
    "        plt.scatter(df1[feature], df2[feature], marker='+')\n",
    "        plt.xlabel(feature, fontsize=9)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['var_0', 'var_1','var_2','var_3', 'var_4', 'var_5', 'var_6', 'var_7', \n",
    "           'var_8', 'var_9', 'var_10','var_11','var_12', 'var_13', 'var_14', 'var_15', \n",
    "           ]\n",
    "plot_feature_scatter(train_df[::20],test_df[::20], features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x920ec0f5f8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df['target'], palette='Set3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10.1% target values with 1\n"
     ]
    }
   ],
   "source": [
    "print(\"There are {}% target values with 1\".format(100 * train_df[\"target\"].value_counts()[1]/train_df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,10))\n",
    "train_df[train_df['target']==0].var_0.plot.hist(ax=ax[0],bins=20,edgecolor='black',color='red')\n",
    "ax[0].set_title('target= 0')\n",
    "x1=list(range(0,85,5))\n",
    "ax[0].set_xticks(x1)\n",
    "train_df[train_df['target']==1].var_0.plot.hist(ax=ax[1],color='green',bins=20,edgecolor='black')\n",
    "ax[1].set_title('target= 1')\n",
    "x2=list(range(0,85,5))\n",
    "ax[1].set_xticks(x2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[train_df.columns[2:]].mean().plot('hist');plt.title('Mean Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "train_df['target'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
    "ax[0].set_title('target')\n",
    "ax[0].set_ylabel('')\n",
    "sns.countplot('target',data=train_df,ax=ax[1])\n",
    "ax[1].set_title('target')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df[\"var_0\"].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_missing_data(df):\n",
    "    flag=df.isna().sum().any()\n",
    "    if flag==True:\n",
    "        total = df.isnull().sum()\n",
    "        percent = (df.isnull().sum())/(df.isnull().count()*100)\n",
    "        output = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "        data_type = []\n",
    "        # written by MJ Bahmani\n",
    "        for col in df.columns:\n",
    "            dtype = str(df[col].dtype)\n",
    "            data_type.append(dtype)\n",
    "        output['Types'] = data_type\n",
    "        return(np.transpose(output))\n",
    "    else:\n",
    "        return(False)\n",
    "check_missing_data(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3596\n",
       "1     404\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_balance(df,target):\n",
    "    check=[]\n",
    "    # written by MJ Bahmani for binary target\n",
    "    print('size of data is:',df.shape[0] )\n",
    "    for i in [0,1]:\n",
    "        print('for target  {} ='.format(i))\n",
    "        print(df[target].value_counts()[i]/df.shape[0]*100,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of data is: 4000\n",
      "for target  0 =\n",
      "89.9 %\n",
      "for target  1 =\n",
      "10.100000000000001 %\n"
     ]
    }
   ],
   "source": [
    "check_balance(train_df,'target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 2.649264\n",
      "Kurtosis: 5.021111\n"
     ]
    }
   ],
   "source": [
    "print(\"Skewness: %f\" % train_df['target'].skew())\n",
    "print(\"Kurtosis: %f\" % train_df['target'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[\"target\",\"ID_code\"]\n",
    "X = train_df.drop(cols,axis=1)\n",
    "y = train_df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test  = test_df.drop(\"ID_code\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 4.17396075e-03  3.64648001e-03  6.10663303e-03  3.75836219e-03\n",
      " -4.53371598e-04  8.10091970e-04  1.95799845e-02  4.22157849e-03\n",
      "  6.26657726e-04 -4.35028778e-03 -4.00727364e-04 -4.71710261e-04\n",
      " -5.57426749e-02 -2.41050148e-03 -4.55340468e-03  2.98592270e-02\n",
      "  4.59145459e-04  6.88466609e-04  1.41048080e-03  3.64272667e-04\n",
      " -1.26741971e-03 -1.80775200e-03  6.72560119e-03 -2.52061902e-02\n",
      "  2.85656067e-03 -4.85989956e-03  1.73972087e-03 -2.39010421e-03\n",
      " -2.01497584e-02 -3.93179208e-04  1.02848565e-04 -9.71253997e-04\n",
      "  1.04826516e-03 -2.18011012e-03 -2.81068656e-02  3.13316566e-03\n",
      " -2.24741916e-03  8.81884537e-04  9.69637236e-05 -5.62671522e-04\n",
      "  2.23655546e-03 -8.15640873e-04 -1.12606678e-02 -2.21792482e-02\n",
      " -3.17631368e-03  3.16871761e-05  1.46407911e-04 -2.58863042e-05\n",
      "  1.23431405e-03  1.02443938e-03 -7.67608844e-03  5.36263300e-04\n",
      "  1.04074823e-03  1.62604402e-02 -3.12518857e-04  1.65308548e-03\n",
      " -1.38557377e-03 -4.44989344e-03 -4.98560497e-04 -7.50266006e-03\n",
      "  1.01124643e-03  8.36962141e-04  2.45842274e-03 -1.72969257e-03\n",
      " -8.19122383e-04  3.19450457e-04  8.99051447e-03  5.53216055e-04\n",
      " -9.44804634e-01 -9.38659588e-04  9.32857808e-04  5.11579410e-02\n",
      " -1.22262467e-03 -4.49682164e-04  1.00043360e-05 -1.85937809e-03\n",
      " -3.21107352e-03 -1.64620844e-03  4.20041041e-03 -1.20470720e-03\n",
      " -1.81553788e-03 -1.26347562e-02  1.73131153e-04 -9.62839397e-04\n",
      "  4.76205304e-04 -1.58436798e-03 -1.68328270e-03 -6.49054067e-04\n",
      " -4.17023396e-03  1.27994674e-03  8.31201040e-04  1.73281680e-02\n",
      " -3.59179794e-03 -2.15099175e-02  5.38184794e-03  8.36300135e-03\n",
      "  9.81470674e-04  6.73772368e-04 -4.06923445e-03  6.89706683e-03\n",
      " -5.46589966e-04  1.29823681e-03 -1.19779912e-03 -1.53327912e-02\n",
      " -7.40472142e-03  2.05164398e-03  4.54984728e-03 -1.22497558e-03\n",
      " -7.51703445e-02 -3.27619286e-03  4.61730803e-03  9.82899956e-03\n",
      "  2.50076603e-03  1.88244258e-04 -1.05824790e-02 -5.57604484e-03\n",
      " -2.02609326e-03  7.24734448e-04  1.09849460e-03  3.18005274e-03\n",
      "  2.42527482e-04 -7.93842485e-03 -3.03570365e-03 -2.54122832e-03\n",
      " -2.04888230e-03  3.44042727e-03  2.83330204e-03 -4.55351002e-03\n",
      "  1.62593317e-03  1.04584617e-03  1.03275795e-02 -1.43622500e-02\n",
      " -1.00255905e-02  5.30426380e-02  2.85150100e-04  9.60198519e-04\n",
      " -2.08580845e-04 -1.38241196e-04  1.82287063e-03 -3.33906067e-03\n",
      "  5.86820545e-04  2.27828305e-04  2.38992721e-04 -7.47890489e-04\n",
      "  7.61123151e-03  3.49677440e-03 -6.42094430e-03  1.96783703e-03\n",
      " -5.49248002e-02 -1.29429470e-03 -3.03923625e-03  2.69749376e-03\n",
      " -1.82341675e-03  5.82732865e-04 -3.08290514e-03  2.01226765e-03\n",
      " -9.99373811e-04  1.94662079e-03  1.25720814e-04  3.34263980e-03\n",
      "  4.53455804e-04 -2.93169108e-02  8.41714876e-03  6.51881975e-04\n",
      "  1.53954150e-03 -1.20093053e-03 -4.44865820e-02  1.62250237e-03\n",
      " -1.21020392e-04 -2.88247231e-02  1.66943961e-03  2.56499061e-04\n",
      " -9.29798314e-04  1.62597241e-03 -2.17690554e-03  1.44275272e-03\n",
      "  5.66072864e-04 -2.02954515e-03 -3.28238472e-05  4.42505754e-03\n",
      "  2.71709222e-03  2.70530564e-03 -4.46391275e-04 -1.47199922e-03\n",
      "  1.76324211e-03  7.79495453e-05 -2.65811392e-03  4.12342471e-04\n",
      " -1.94087956e-03  8.48438367e-03  4.42990168e-03  3.07994495e-03\n",
      " -8.47302921e-03  4.52251500e-04 -2.42868790e-03  9.32395296e-03\n",
      "  1.30920666e-03 -8.21693064e-03 -4.68033878e-03  4.67824171e-04] \n",
      "\n",
      "[0.19410429 0.18383761 0.19339692 0.06300603 0.24224965 0.20597222\n",
      " 0.05434192 0.10535237 0.20911286 0.13061029]\n",
      "0.15819841874902663\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import model_selection\n",
    "kfold = model_selection.KFold(n_splits=10)\n",
    "lr = LinearRegression()\n",
    "scoring = 'r2'\n",
    "results = model_selection.cross_val_score(lr, X, y, cv=kfold, scoring=scoring)\n",
    "lr.fit(X,y)\n",
    "lr_predictions = lr.predict(X_test)\n",
    "print('Coefficients: \\n', lr.coef_,'\\n')\n",
    "print(results)\n",
    "print(results.sum()/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square score:  0.2682567195512986\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(\"R_square score: \", r2_score(y_test,lr_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.80523082 -1.01801802 -0.5671147  -1.1978022  -0.69370074 -0.62754304\n",
      " -1.37837838 -0.90998043 -0.59122416 -1.18086653]\n",
      "-0.8969859005637171\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dtr = DecisionTreeRegressor(random_state = 42)\n",
    "dtr.fit(X,y)\n",
    "dtr_predictions = dtr.predict(X_test) \n",
    "results = model_selection.cross_val_score(dtr, X, y, cv=kfold, scoring='r2')\n",
    "print(results)\n",
    "print(results.sum()/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square score:  1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"R_square score: \", r2_score(y_test,dtr_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.80523082 -1.01801802 -0.5671147  -1.1978022  -0.69370074 -0.62754304\n",
      " -1.37837838 -0.90998043 -0.59122416 -1.18086653]\n",
      "-0.8969859005637171\n",
      "R_square score:  0.0799477213544072\n"
     ]
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "clf = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth = 5, min_samples_split = 2,\n",
    "          learning_rate = 0.1, loss = 'ls')\n",
    "clf.fit(X_train, y_train)\n",
    "clf_predicitions = clf.predict(X_test) \n",
    "results = model_selection.cross_val_score(dtr, X, y, cv=kfold, scoring='r2')\n",
    "print(results)\n",
    "print(results.sum()/10)\n",
    "print(\"R_square score: \", r2_score(y_test,clf_predicitions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.80523082 -1.01801802 -0.5671147  -1.1978022  -0.69370074 -0.62754304\n",
      " -1.37837838 -0.90998043 -0.59122416 -1.18086653]\n",
      "-0.8969859005637171\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfr = RandomForestRegressor(n_estimators = 100)\n",
    "rfr.fit(X_train,y_train)\n",
    "rfr_predicitions = rfr.predict(X_test) \n",
    "results = model_selection.cross_val_score(dtr, X, y, cv=kfold, scoring='r2')\n",
    "print(results)\n",
    "print(results.sum()/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_square score:  0.05563228777985085\n"
     ]
    }
   ],
   "source": [
    "print(\"R_square score: \", r2_score(y_test,rfr_predicitions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.array([r2_score(y_test,lr_predictions),r2_score(y_test,dtr_predictions),r2_score(y_test,rfr_predicitions),\n",
    "           r2_score(y_test,clf_predicitions)])\n",
    "x = [\"LinearRegression\",\"RandomForest\",\"DecisionTree\",\"Gradient Boost\"]\n",
    "plt.bar(x,y)\n",
    "plt.title(\"Comparison of Regression Algorithms\")\n",
    "plt.ylabel(\"r2_score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
